Retail Price Comparison APIs  Scraping BigBasket, Blinkit & Zepto
                     Retail Price Comparison 
APIs: Scraping BigBasket, 
Blinkit & Zepto
Introduction
In the era of digital retail and hyperlocal commerce, 
dynamic pricing is the new norm. Platforms such as 
BigBasket, Blinkit, and Zepto compete aggressively not 
just on delivery speed and product range, but on pricing 
and promotions as well. But how can retailers, brands, 
analysts, or even consumers determine the most 
competitive price for the same SKU across these 
platforms? This is where Retail Price Comparison APIs 
come in — often powered by Web Scraping Services, 
mobile data extraction, or reverse-engineered endpoints 
— to provide structured, real-time pricing and inventory 
data that fuels decision-making.
This research-oriented blog will explore:
• The concept and need for retail price comparison APIs
• Technical approaches to collecting pricing data from 
BigBasket, Blinkit and Zepto
• Data normalization, SKU matching and challenges
• Use cases across industries
• Legal and ethical considerations
• Future trends in retail analytics
Why Retail Price Comparison Matters
Simply put, Price Comparison enables stakeholders — 
from internal pricing teams to external analytics 
platforms — to benchmark product prices across 
competitors to make informed decisions.
In the highly competitive quick-commerce segment in 
India, daily fluctuations in prices, flash deals, pin code-
specific offers, and dynamic inventory make price data 
both complex and valuable.
A typical case might involve comparing prices of a set of 
standard SKUs — such as rice, cooking oil, sugar, 
potatoes, and eggs — across platforms like Blinkit, Zepto, 
Instamart, and BigBasket. Real data analysis can show 
significant differences in pricing even for identical items, 
providing strategic insight into competitor pricing and 
discount strategies.
Price comparison is essential for:
• Retailers – to adjust pricing dynamically and optimize 
margins
• Brands – to monitor competitor discounting trends and 
avoid margin erosion
• Market analysts & data scientists – to generate 
historical pricing trends and forecasts
• Consumers – to pick the most cost-effective platform for 
their needs
Approaches to Collecting Price Data
APIs vs. Web Scraping
In an ideal world, platforms would offer public APIs 
designed for price comparison — but most grocery 
marketplaces do not provide open API access to pricing 
data. Instead:
• Official APIs may be restricted or private.
• Third-party or reverse-engineered APIs are often used to 
extract JSON data endpoints.
• Web scraping and mobile app data extraction are the 
most common approaches for structured pricing feeds.
Web scraping API involves simulating the requests made 
by the app or browser to pull product catalogue 
responses, prices, discounts, availability, and other meta 
data. These responses are then parsed and structured 
into JSON or CSV for analysis. In many research and 
commercial implementations, highly customized scraping 
pipelines or REST-ful microservices are deployed to 
inventory these endpoints across cities and pin codes.
Profiled and Geo-Fencing Techniques
Hyperlocal pricing means that the same SKU may have 
different prices depending on the pin code or delivery 
zone. To handle this:
• Scrapers are configured with geographic session 
simulation — often using proxy networks — to mimic 
requests from different ZIP codes.
• Mobile app endpoints (e.g., Android or iOS catalogue 
APIs) are often targeted because they tend to be more 
structured and include inventory and offer data.
Challenges in Generating Unified Price Feeds
While APIs or scraping engines can fetch raw data, 
meaningful comparison requires standardized processing. 
Key challenges include:
SKU Matching
Platforms often list products with slight variations in:
• Brand names and descriptions
• Units (e.g., 500 ml vs 1 liter)
• Variant names and packaging
Fuzzy matching algorithms using product names, brand 
identifiers, and packaging sizes are essential to align 
SKUs across platforms into a common taxonomy. This 
process typically involves tokenization, similarity scoring, 
and threshold-based grouping to avoid mismatched 
comparisons.
Rapid Structural Changes
Grocery apps regularly update:
• JSON endpoints
• UI styling
• Endpoint parameters
These changes can break scraping pipelines, requiring 
frequent maintenance and adaptability in code.
Dynamic Pricing & Flash Deals
Quick commerce or Grocery Datasets platforms are 
notorious for micro-discounts and flash deals that change 
hourly. Monitoring these requires:
• High-frequency polling
• Infrastructure capable of capturing these shifts
Without real-time pipelines, analytics may miss the true 
pricing behavior.
Technical Architecture of Price Comparison 
APIs
Here's a simplified typical architecture used to generate a 
retail price comparison API:
1. Data Collection Layer
• Custom scrapers or reverse-engineered endpoints to pull 
pricing and offer data from Blinkit / Zepto / BigBasket.
• Launch multiple threads or servers to collect region-
specific data (e.g., pin codes).
2. Data Normalization & Storage
• Raw JSON responses are cleansed and normalized into a 
unified schema (SKU, price, discount, platform, 
timestamp).
• Data is stored in databases such as PostgreSQL, 
MongoDB, or even cloud data warehouses.
3. API Layer
• A REST API or GraphQL interface that serves real-time or 
cached price comparison.
• Optional filtering by category, city, brand, or pin code.
4. Analytics & Visualization
• Dashboards, heatmaps, time series analytics, and 
historical trends.
• Integration with business intelligence tools like Tableau 
or Power BI.
5. Alerting & Monitoring
• Price anomaly detection (e.g., sudden spike/drop alerts).
• Weekly or daily reports on competitor pricing trends.
In real commercial scenarios, some projects have 
deployed cloud functions and serverless crawlers to 
support real-time feeds with minimal latency.
Use Cases for Retail Price Comparison APIs
Dynamic Pricing & Competitive Intelligence
Retailers can feed real-time competitor pricing into their 
pricing engines to:
• Adjust prices automatically within target margins
• Respond to discount patterns quickly
• Avoid undercutting or overpricing in hyper-competitive 
markets
For example, grocery brands have leveraged web 
scraping engines to monitor daily pricing changes, stock 
status, and competitor offers across 30+ cities. These 
insights help in aligning price strategy with consumer 
expectations.
Inventory & SKU Forecasting
Price comparison data — especially when paired with 
availability data — can enhance forecasting models to 
account for stockouts, shelf availability, and seasonality.
Consumer Apps
Several community-driven apps (e.g., Comparify) 
aggregate price feeds from Blinkit, Zepto, Instamart, and 
BigBasket to offer users real-time comparative pricing and 
delivery time predictions. Although these projects 
highlight demand, they also illustrate access challenges 
due to closed APIs and data restrictions.
Legal and Ethical Considerations
Technical feasibility doesn't always equate to legal 
clearance. Most major grocery platforms do not expose 
public pricing APIs, and scraping is often explicitly 
restricted in their Terms of Service. Developers and 
companies should consider:
• Terms of Service Compliance — Understanding what the 
platform's terms allow or prohibit.
• Robots.txt Respect — While robots exclusion protocols 
are standard for web pages, app endpoints often lack 
clear robots.txt guidelines.
• User Privacy & GDPR/IT Rules — Data collection must 
avoid personal data unless explicitly permitted.
• Consent & Partnerships — The safest route is engaging in 
formal partnerships or licensing data access from 
platforms.
In developer forums, contributors often note the challenge 
of legally obtaining real-time grocery data because of 
these restrictions.
Case Study Examples
City-Level Price Intelligence
Large FMCG clients have used price scraping APIs to build 
hyperlocal dashboards showing:
• Price per SKU across cities
• Discount intensity and patterns
• Region-wise availability constraints
This enables rapid decision-making for promotions and 
stock allocation.
Real-Time Dynamic Pricing Engines
Companies have integrated automated grocery price APIs 
into their pricing engines, allowing structures like:
• Real-time alerts on competitor price drops
• End-of-day repricing based on historical trends and 
competitor discounts
• SKU ranking by price competitiveness for promotional 
triggers
Data Analysis and Insights
Analysis across platforms often reveals patterns such as:
• Platform-specific pricing behaviors — e.g., Blinkit being 
cheaper on certain staples, while Zepto has dynamic flash 
pricing.
• Regional fluctuations — where prices vary significantly 
between metros and satellite cities.
• Discount depth vs real baseline price — data shows that 
deeper print discounts don't always translate to real 
savings if the baseline price was inflated.
These insights illustrate why simple price comparisons are 
inadequate without robust data normalization, historical 
context, and statistical interpretation.
Future Directions in Retail Price Analytics
AI-Driven Prediction Models
With structured price comparison APIs, AI models can 
forecast pricing trends based on seasonality, demand 
shifts, and promotional cycles.
Enhanced Product Matching with ML
Future pipelines will rely more on machine learning models 
(BERT, embeddings) to improve SKU matching between 
platforms — reducing false positives and improving 
accuracy.
Consumer-Facing Smart Assistants
Apps that automatically suggest the best platform for 
each whole cart rather than individual items are in 
development, using richer analytics that consider delivery 
fees and loyalty offers.
Ethical Data Collaborations
Expect more negotiated partnerships where platforms 
allow vetted third-party access to pricing data via licensed 
APIs — lowering risks associated with scraping.
Conclusion
Retail price comparison APIs — particularly for Indian 
grocery platforms like BigBasket, Blinkit, and Zepto — are 
powerful tools for competitive pricing, inventory planning, 
and real-time decision support. However, building such 
systems comes with notable technical, legal, and ethical 
challenges:
• Platforms often lack public APIs, pushing builders toward 
scraping.
• Data must be normalized across SKU variations, pin code 
specifics, and frequent structural changes.
• Studies and case deployments show that robust price 
comparison yields actionable insights — from competitor 
discount strategies to dynamic pricing triggers.
Looking ahead, data collaboration and AI-augmented 
analytics will further enhance how Real Data API helps 
retailers monitor pricing across the hyper-competitive 
quick-commerce landscape.
Source: 
https://www.realdataapi.com/retail-price-compariso
n-apis-scraping-bigbasket-blinkit-zepto.php